Evaluations of training paradigms in neural image captioning
This project presents an implementation of 2 neural image captioning model which shall achieve results comparable to state of the art models. The 2 models are composed of different training paradigm, cross entropy training or self-critical training: • Model-1: Bottom-up attention with Self-Critical...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2019
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| Online Access: | https://hdl.handle.net/10356/136508 |
| _version_ | 1811680966151766016 |
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| author | Lee, Si Min |
| author2 | Zhang Hanwang |
| author_facet | Zhang Hanwang Lee, Si Min |
| author_sort | Lee, Si Min |
| collection | NTU |
| description | This project presents an implementation of 2 neural image captioning model which shall achieve results comparable to state of the art models. The 2 models are composed of different training paradigm, cross entropy training or self-critical training: • Model-1: Bottom-up attention with Self-Critical Training • Model-2: Bottom-up attention with cross entropy Training There will be a comparison and evaluation of the results against each model and related neural image captioning models to determine the best performing model. |
| first_indexed | 2024-10-01T03:33:26Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/136508 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:33:26Z |
| publishDate | 2019 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1365082019-12-20T07:44:00Z Evaluations of training paradigms in neural image captioning Lee, Si Min Zhang Hanwang School of Computer Science and Engineering hanwangzhang@ntu.edu.sg Engineering Engineering::Computer science and engineering This project presents an implementation of 2 neural image captioning model which shall achieve results comparable to state of the art models. The 2 models are composed of different training paradigm, cross entropy training or self-critical training: • Model-1: Bottom-up attention with Self-Critical Training • Model-2: Bottom-up attention with cross entropy Training There will be a comparison and evaluation of the results against each model and related neural image captioning models to determine the best performing model. Bachelor of Engineering (Computer Science) 2019-12-20T07:44:00Z 2019-12-20T07:44:00Z 2019 Final Year Project (FYP) https://hdl.handle.net/10356/136508 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering Engineering::Computer science and engineering Lee, Si Min Evaluations of training paradigms in neural image captioning |
| title | Evaluations of training paradigms in neural image captioning |
| title_full | Evaluations of training paradigms in neural image captioning |
| title_fullStr | Evaluations of training paradigms in neural image captioning |
| title_full_unstemmed | Evaluations of training paradigms in neural image captioning |
| title_short | Evaluations of training paradigms in neural image captioning |
| title_sort | evaluations of training paradigms in neural image captioning |
| topic | Engineering Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/136508 |
| work_keys_str_mv | AT leesimin evaluationsoftrainingparadigmsinneuralimagecaptioning |